1.1. Probability Definition [10 - 15 min]*

The formal definition of a probability

1.2. From frequency to relative frequency [20 - 30 min]*

Gives a detailed step-by-step procedure of how to compute a probability from frequency

1.3. From probability  to PMF and CDF [10 - 20 min]*

Show how  to compute a probability mass function (PMF) and a cumulated distribution function (CDF) 

1.4. From tables to probabilities [20-30 min]

Computing probability using data organized into a Table.

2.1. Newcomb-Benford Law [10 - 15 min]*

Statistical Law that could help to find fraud.

2.2. Python on Newcomb-Benford Law [30 - 40 min]*

Understanding how to apply the Newcomb-Benford Law using Python code on data.

2.3. Mirror Statistics to find frauds [25 - 35 min]*

Shows how some simple statistical values could help to find some evidence of possible problems 

2.4. Applying mirror statistics with Python [20-30 min]

A Python code is applied to some databases and helps find mirror statistics.

3.1. Newcomb-Benford Law on Customs databases with Python [10 - 15 min]*

Employing Benford's Law on customs databases using Python Code.

3.2. Applying Benford's Law on De Minimis with Python [30 - 40 min]*

Employing Benford's Law on customs databases using Python Code.

3.3. Applying Mirror Statistics to find frauds with Python [25 - 35 min]*

Using Python code to find some evidence of possible issues. 

2.4. Applying mirror statistics with Python [20-30 min]

Creating Python code to analyze some databases and help find mirror statistics.